Abstract
Generation and transmission unit outages cause MW line overloading and bus voltage magnitude violation problems in the remaining part of the system. This paper presents a new method for bus voltage magnitude screening for transmission line and transformer outages. Classification and ranking errors of sensitivity-based methods are overcome by imposing a non-linear constrained optimization module on the well-known linear feed-forward computational algorithms. Genetic algorithms are preferred as computation tool because of its implementation simplicity as well as for its parallel processing advantage in future computers. Accuracy of the method is tested on IEEE 57-bus test system and IEEE 118-bus test system. The results are compared with those of the full load flow to verify the strength of the method proposed in this paper.
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References
Ejebe GC, Wollenberg BF (1979) Automatic contingency selection. IEEE Trans PAS PAS-98: 92–104
Milkolinnas TA, Wollenberg BF (1981) An advanced contingency selection algorithm. IEEE Trans PAS PAS-100: 608–617
Lauby MG, Milkolinnas TA, Reppen ND (1983) Contingency selection of branch outages causing voltage problems. IEEE Trans on PAS PAS-102: 608–612
Chen Y, Bose A (1989) Direct ranking for voltage conting. IEEE Trans Power Syst PWRS-4: 1335–1344
Brandwajn V, Lauby MG (1989) Complete bounding method for AC contingency screening. IEEE Trans Power Syst 4: 724–729
Schafer KF, Verstege JF (1990) Adaptive procedure for masking effect compensation in contingency selection algorithm. IEEE Trans Power Syst 5: 539–546
Albuyeh F, Bose A, Heath B (1982) Reactive power considerations in automatic contingency selection. IEEE Trans PAS PAS-101: 107–112
Nara K, Tanaka K, Kodama H et al (1985) On-line contingency Selection for voltage security analysis. IEEE Trans PAS PAS-104:847–856
Ruiz PA, Sauer PW (2007) Voltage and reactive power estimation for contingency analysis using sensitivities. IEEE Trans PAS 22(2): 639–647
Galiana FD (1984) Bound estimates of the severity of line outages in power system analysis and ranking. IEEE Trans PAS PAS-103: 2612–2622
Brandwajn V (1988) Efficient bounding method for linear contingency analysis. IEEE Trans Power Systems 3: 38–43
Meliopoulos APS, Cheng CS, Xia F (1994) Performance evaluation of static security analysis methods. IEEE Trans Power Syst 9: 1441–1449
Hsu YY, Kuo HC (1992) Fuzzy set based contingency ranking. IEEE Trans Power Syst 7: 1189–1195
Srivastava L, Singh SN, Sharma J (1999) Knowledge-based neural network for voltage contingency selection and ranking. IEE Proc C 146(6): 640–656
Sidhu TS, Cui L (2000) Contingency screening for steady state security analysis by using FFT and artificial neural networks. IEEE Trans Power Syst 15: 421–426
Swarup KS, Sudhakara G (2006) Neural network approach to contingency screening and ranking in power systems. Neurocomputing 70(1–3): 105–118
Ozdemir A, Lim JY, Singh C (2003) Branch outage simulation for MWAR flows: bounded network solution. IEEE Trans Power Syst 18(4): 1523–1528
Ozdemir A, Lim JY, Singh C (2005) Post-outage reactive power flow calculations by genetic algorithms: constrained optimization approach. IEEE Trans Power Syst 20(3): 1266–1272
Gen M, Cheng R (2000) Genetic algorithms and engineering optimization. Wiley, New York
Zimmerman R, Gan D (1997) Matpower manual. PSERC Cornell University, USA
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Ozdemir, A., Singh, C. Voltage magnitude contingency screening by constrained optimization method. Electr Eng 94, 241–248 (2012). https://doi.org/10.1007/s00202-012-0238-6
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DOI: https://doi.org/10.1007/s00202-012-0238-6